Symptom-based classification of 16p11.2 copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods

dc.authorid0000-0001-6574-8149
dc.authorid0000-0002-4574-421X
dc.authorid0000-0002-0220-1207
dc.authorid0000-0001-9881-6013
dc.authorid0000-0002-0578-3126
dc.contributor.authorBolat, Hilmi
dc.contributor.authorBulut, Edanur
dc.contributor.authorÜnsel-bolat, Gül
dc.contributor.authorÖzgül, Semiha
dc.contributor.authorTuran, Duygu Selin
dc.contributor.authorÇeli̇k, Samet
dc.contributor.authorKoyuncu, Özgür Ozan
dc.date.accessioned2026-06-23T06:27:02Z
dc.date.issued2026
dc.departmentFakülteler, Tıp Fakültesi, Dahili Tıp Bilimleri Bölümü
dc.descriptionBolat, Hilmi - Bulut Edanur (Balikesir Author)
dc.description.abstractPurpose: Copy number variations (CNVs) in the 16p11.2 region are well-established contributors to neurodevelopmental disorders, yet phenotype variability across this locus remains insuffi ciently characterized. This study investigates clinical features and ASD-related symptoms among carriers of rare pathogenic and common CNVs, and evaluates symptom-level discriminability using machine learning (ML) methods. Methods: Genetic data from 7568 individuals were retrospectively screened, identifying 147 carriers of 16p11.2 CNVs. Detailed clinical assessments were completed for 50 participants. ASDrelated symptoms were evaluated using a structured 25-item instrument. Group comparisons applied nonparametric statistics with effect sizes, confidence intervals, and FDR correction. ML analyses used PCA and k-means for feature selection, oversampling methods (SMOTE, BorderlineSMOTE, ADASYN), and five classifiers, evaluated through cross-validation. Results: Across pathogenic and common CNV groups, no significant differences were observed in social communication, restricted/repetitive behaviors, sensory symptoms, regression, or total autism scores (FDR-adjusted p > 0.05). Aggression was more frequently endorsed in pathogenic CNV carriers (raw p = 0.030; FDR p = 0.098). BMI was higher in pathogenic CNVs, though nonsignificant after correction (raw p = 0.027; FDR p = 0.152). ML analyses identified three recurrent discriminative symptoms across multiple datasets: delayed response to name, unusual object play, and aggression. Dataset 3 (16 symptoms) provided the most balanced classification performance but, given the very small pathogenic CNV sample, results remain exploratory. Conclusion: Findings suggest that, while most autism-related symptoms do not differ between groups, aggression and increased BMI may represent preliminary phenotypic signals associated with pathogenic CNVs. Integrating clinical data from 147 CNV carriers further supports a po tential widespread effect across the broader 16p11.2 locus rather than a single breakpoint-specific mechanism. However, all results should be interpreted cautiously due to limited sample size, and larger, systematically phenotyped cohorts are required to establish robust genotype–phenotype relationships.
dc.description.sponsorshipTurkiye Bilimsel ve Teknolojik Arastirma Kurumu (TUBITAK) 321S239
dc.identifier.doi10.1016/j.reia.2026.202865
dc.identifier.endpage12
dc.identifier.issn3050-6573
dc.identifier.scopus2-s2.0-105030284433
dc.identifier.scopusqualityQ2
dc.identifier.startpage1
dc.identifier.urihttps://doi.org/10.1016/j.reia.2026.202865
dc.identifier.uri3050-6565
dc.identifier.urihttps://hdl.handle.net/20.500.12462/24092
dc.identifier.volume132
dc.identifier.wosWOS:001700449100001
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherElsevier Ltd
dc.relation.ispartofResearch in Autism
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.relation.tubitak321S239
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subject16p11.2
dc.subjectCNVs
dc.subjectMachine Learning
dc.subjectNeurodevelopmental Disorders
dc.titleSymptom-based classification of 16p11.2 copy number variations underlying the multidimensional autism spectrum disorder phenotype using machine learning methods
dc.typeArticle

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